勇闖新世界︰ W!o《卡夫卡村》變形祭︰感知自然‧數據分析‧八下

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勇闖新世界︰ W!o《卡夫卡村》變形祭︰感知自然‧溼度》文本中所提之『溫度平均』議題︰

Raspberry Pi Learning Resources

Temperature

The Sense HAT features a number of sensors, including a temperature sensor.

The image above shows a clinical thermometer. You may have been asked to place one in your mouth when you’ve been ill. Notice that the numbers start at 35, so it’s only used for measuring human body temperature. The Sense HAT temperature sensors can measure temperatures from as low as -40 degrees Celsius up to +120 degrees Celsius though, so they are much more versatile than a clinical thermometer. The Sense HAT has two temperature sensors. One is built into the humidity sensor and the other is built into the pressure sensor. You can choose which one to use, or you could use both and average the result.

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『溫度』文本,所謂『平均』的說法是否『合適』的呢?這又該用什麼『原則』來判斷的耶??

【用什麼度量溫度?】

pi@raspberrypi ~ sudo python3 Python 3.2.3 (default, Mar  1 2013, 11:53:50)  [GCC 4.6.3] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from sense_hat import SenseHat >>> 感測 = SenseHat()  # 壓力計量溫度 >>> 溫度_壓力計 = 感測.get_temperature_from_pressure() >>> print("溫度: %s C" % 溫度_壓力計) 溫度: 30.652084350585938 C >>> 壓力 = 感測.get_pressure() >>> print("壓力: %s Millibars" % 壓力) 壓力: 1008.718994140625 Millibars  # 溼度計量溫度 >>> 溫度_溼度計 = 感測.get_temperature_from_humidity() >>> print("溫度: %s C" % 溫度_溼度計) 溫度: 30.774192810058594 C >>> 溼度 = 感測.get_humidity() >>> print("溼度: %s %%rH" % 溼度) 溼度: 70.17244720458984 %rH >>>  </pre> ───     <span style="color: #808000;">不知讀者是否已經有了答案的呢??僅就比較</span>  <span style="color: #808080;">【壓力之溫度感測器】︰ 0 - 65^{\circ}  C \  \pm 2^{\circ} C</span>  <span style="color: #808080;">【溼度之溫度感測器】︰ 0 - 60^{\circ}  C \  \pm 1^{\circ} C</span>  <span style="color: #808000;">準確度而言,這個『平均』的作法恐怕是無攸利的吧!或知在相對溼度的量測中,溫度是個重要的因子乎!!因此必須考慮它的影響 ,所以工廠的『校準』方才會有這麼大的一張表︰</span>  <a href="http://www.freesandal.org/wp-content/uploads/Humidity-Temperature-data-conversion.png"><img class="alignnone size-full wp-image-42450" src="http://www.freesandal.org/wp-content/uploads/Humidity-Temperature-data-conversion.png" alt="Humidity-Temperature-data-conversion" width="988" height="1194" /></a>  <span style="color: #808000;"> </span>  <span style="color: #808000;">那麼什麼是『<a style="color: #808000;" href="https://zh.wikipedia.org/zh-tw/%E6%A0%A1%E5%87%86">校準</a>』 <a style="color: #808000;" href="https://en.wikipedia.org/wiki/Calibration">Calibration</a> 的呢?維基百科的詞條講︰</span>  <b>Calibration</b> is process of finding a relationship between two unknown (when the measurable quantities are not given a particular value for the amount considered or found a standard for the quantity) quantities. When one of quantity is known, which is made or set with one device, another measurement is made as similar way as possible with the first device using a second device.The measurable quantities may differ in two devices which are equivalent. The device with the known or assigned correctness is called the <a title="Standard (metrology)" href="https://en.wikipedia.org/wiki/Standard_%28metrology%29">standard</a>. The second device is the <a title="Device under test" href="https://en.wikipedia.org/wiki/Device_under_test">unit under test</a>, test instrument, or any of several other names for the device being calibrated.  The formal definition of calibration by the <a title="International Bureau of Weights and Measures" href="https://en.wikipedia.org/wiki/International_Bureau_of_Weights_and_Measures">International Bureau of Weights and Measures</a> is the following: "Operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties (of the calibrated instrument or secondary standard) and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication."<sup id="cite_ref-metrology_terms_1-0" class="reference"><a href="https://en.wikipedia.org/wiki/Calibration#cite_note-metrology_terms-1">[1]</a></sup>  <a href="http://www.freesandal.org/wp-content/uploads/Avery_postal_scale.jpg"><img class="alignnone size-full wp-image-42493" src="http://www.freesandal.org/wp-content/uploads/Avery_postal_scale.jpg" alt="Avery_postal_scale" width="170" height="257" /></a>  An example of a device whose calibration is off: a <a title="Weighing scale" href="https://en.wikipedia.org/wiki/Weighing_scale">weighing scale</a> that reads ½ ounce without any load.  ───     <span style="color: #808000;">簡單的說就是︰選擇已知『精準度』的 □□ 物理量之量測儀器為『標準』,在相同的條件下,用其對此 □□ 物理量之『度量值』作『基礎』,建立其與『待校正設備』對這同一 □□ 物理量之 ○○ 『量測值』的『數值關係』。這個  ○○ 『量測值』就是由該設備之『量測方法』所決定的。比方說︰</span>  <span style="color: #808000;">【壓力感測器用電阻】</span>  <a href="http://www.freesandal.org/wp-content/uploads/基本壓力感測器構造.png"><img class="alignnone size-full wp-image-41373" src="http://www.freesandal.org/wp-content/uploads/基本壓力感測器構造.png" alt="基本壓力感測器構造" width="517" height="541" /></a>     <span style="color: #808000;">【溼度感測器用電容】</span>  <a href="http://www.freesandal.org/wp-content/uploads/溼度感測器工作原理.png"><img class="alignnone size-full wp-image-41984" src="http://www.freesandal.org/wp-content/uploads/溼度感測器工作原理.png" alt="溼度感測器工作原理" width="1076" height="640" /></a>     <span style="color: #808000;">若是 □□ 物理量與 ○○ 物理量之間是『線性關係』</span> \Box = \alpha \cdot \bigcirc + \beta<span style="color: #808000;">,那麼一般可用『兩點』來作校正。如果『量測模型』是『非線性 』的,就得作『多點』校準的了。由於『微機電』裝置的『尺寸』很小,因此 ○○ 物理量 ── 舉例講,電阻或電容 ── 也就非常的小。所以『製造』與『度量』之『精準要求』極高。或可說明那個 ADC 的『 bit 數』為什麼得很大的耶??要是從『標準差』來看 </span>\sigma_{\Box} = \alpha \cdot \sigma_{\bigcirc}<span style="color: #808000;">,也可明白最終『精準度』怎麼會沒有那麼高的了!!</span>  <span style="color: #808000;">如是以《 <a style="color: #808000;" href="https://www.raspberrypi.org/learning/getting-started-with-the-sense-hat/worksheet/">Sensing the environment</a> 》範例中的某部份意譯來看︰</span> <pre class="lang:sh decode:true">pi@raspberrypi ~ python3
Python 3.4.2 (default, Oct 19 2014, 13:31:11) 
[GCC 4.9.1] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from sense_hat import SenseHat
>>> 感測 = SenseHat()
>>> 溫度 = 感測.get_temperature()
>>> 壓力 = 感測.get_pressure()
>>> 溼度 = 感測.get_humidity()

>>> 溫度 = round(溫度, 1)
>>> 壓力 = round(壓力, 1)
>>> 溼度 = round(溼度, 1)

>>> print(溫度, 壓力, 溼度) 
27.9 1017.3 67.5
>>> 

 

,恐怕仍舊是不符合『有效數字』之科學記數法者也。